TASK1 - Iris Flowers Classification ML Project : The iris flowers dataset is a classic example in the field of supervised machine learning and is often considered the "Hello World" of machine learning. It consists of numerical attributes that describe the characteristics of iris flowers and is an ideal dataset for beginners to learn how to load and manipulate data using supervised learning algorithms. As the dataset is relatively small, it can be easily stored in memory without requiring complex transformations or scaling techniques. This project is a great starting point for learning how to classify and predict the species of iris flowers based on their attributes, which is a fundamental problem in machine learning.

TASK4- Image to Pencil Sketch with Python: *Read the image in RGB format using a suitable library such as OpenCV. *Convert the image to grayscale format using the cvtColor function from OpenCV. This will give us a black and white version of the image. *Invert the grayscale image using the bitwise_not function from OpenCV. This will create a negative image of the grayscale version, which can enhance the details of the image. *Apply a Gaussian blur to the inverted grayscale image using the GaussianBlur function from OpenCV. This will create a blurred version of the negative image. *Divide the grayscale image by the blurred image using the divide function from OpenCV. This will create the final pencil sketch version of the image. NOTE that the size of the Gaussian blur kernel (specified by the (21, 21) argument in the code above) and the scale factor used in the divide function can be adjusted to achieve different effects.

TASK5-Exploratory Data Analysis on Dataset As a security or defense analyst, performing exploratory data analysis (EDA) on a terrorism dataset can help identify hot zones of terrorism. By analyzing patterns and trends in the data, we can gain insights into the geographic locations and other characteristics of terrorist incidents.